Next-generation seismic model of the Australian crust from synchronous and asynchronous ambient noise imaging

The proliferation of seismic networks in Australia has laid the groundwork for high-resolution probing of the continental crust. Here we develop an updated 3D shear-velocity model using a large dataset containing nearly 30 years of seismic recordings from over 1600 stations. A recently-developed ambient noise imaging workflow enables improved data analysis by integrating asynchronous arrays across the continent. This model reveals fine-scale crustal structures at a lateral resolution of approximately 1-degree in most parts of the continent, highlighted by 1) shallow low velocities (<3.2 km/s) well correlated with the locations of known sedimentary basins, 2) consistently faster velocities beneath discovered mineral deposits, suggesting a whole-crustal control on the mineral deposition process, and 3) distinctive crustal layering and improved characterization of depth and sharpness of the crust-mantle transition. Our model sheds light on undercover mineral exploration and inspires future multi-disciplinary studies for a more comprehensive understanding of the mineral systems in Australia.

This main contribution of this work is a new high-resolution 3D crustal model of Australia, which is enough for it to be publishable in journals like JGR. The authors claim their model could unlock resource potential in vast swaths of covered areas. However, I do not think this point is well supported. As it is well known, most mineral deposits across Australia are found in the areas with direct outcrops on the surface as thick sedimentary covers obscure the signature of mineral deposits, making potential mineral deposits hard to be discovered. So, it is not a surprise to see most known mineral deposits are founded in areas characterized by high velocities at the shallow depths.
As for the discussions on the upper mantle structures, when I examine the velocities of this model presented in Figure 9, the model of this study differs significantly from the other two models of AuSREM and CSEM-AU; while the latter two are very similar to each other. In addition, given the factor that there are only ~1300 paths of long-period data used in the tomography, I think the structures of the uppermost mantle in this study are not better constrained compared to the other two models. In addition, the discussions on the nature of the Tasman line seem not well supported by the model presented in Figure 9, which does not seem to clearly delineate a major velocity boundary across the Tasman line. I suggest the authors should focus their discussions more on the crustal structures.
Reviewer #3 (Remarks to the Author): In this manuscript the authors developed an Australian wide shallow upper mantle Vs model by using an improved ambient noise tomography approach. Such a continental model was last developed nearly a decade ago. One significance here is the technical advance to apply cross-correlation of conventional noise correlation functions to achieve a relatively more uniform ray coverage for the continent. The resulting Vs model was then used to derive basin thickness and Moho topography across Australia, and then to infer the spatial correlation within known mineral deposits and to propose a tectonic rifting boundary across the Tasman line. The study bridges the gap across the This paper presents a 3-D shear-wave velocity model of the Australian continent based on the latest results of ambient seismic noise tomography inversion. The authors adopted an innovative approach to increase data coverage by reconstructing the noise correlation functions between a pair of temporal stations deployed at different times but overlapped with a few permanent stations. This approach significantly improves the ray-path density that, in turn, leads to higher resolution of the tomography images. With the tomography results, the authors explain the highlights of the new model and discuss their implications for regional tectonics and mineral resources. Overall, this study is a significant step forward in our understanding of the detailed crustal and uppermost mantle velocity structures of the region. Before it is accepted for publication, however, I would like to point out a few issues that should be addressed and/or elaborated in more detail.
First of all, it is unclear how the authors define the ranges of upper, middle, and lower crust based on their results. My impression from Fig. 6 is that they roughly correspond to 0-10, 10-25, and 25-40 km, respectively. However, I think that the authors should be able to quantitatively define the 3-D geometry and thickness variation of the upper, middle, and lower crust for most part of the studied areas. The geometry and thickness variation can also provide important constraints on the characteristics of major geological structures (e.g., cratons, basins, and orogenic belts) and/or regional tectonic evolution.
Secondly, it is confusing when the authors discuss the spatial relationship between velocity anomalies and mineral deposits. Specifically, the authors point out in L145 that giant mineral deposits are preferentially located within 100 km of the craton edge (presumably along the edge of high-velocity anomalies), but later state in L149-150 that the deposits are found near basin margins (i.e., the edge of low-velocity anomalies). The text between L146 and L149 explains that basins can form along craton edge zones due to continental rifting. However, Fig. 1a clearly show that not all basins are located along craton edge zones. Fig. 7 also show that many mineral deposits (especially those in western Australia) are within the high-velocity craton. Thus, I am not sure if a generalized relationship can be derived.
Finally, the comparison of Moho depth with previous models is very useful. But there are cases where the extreme values should be treated with caution. For example, the new model has many places with the Moho depths larger than 55-60 km (Fig. 8). How confident can we trust these values? It is worth noting that some of these extreme values may be related to the way we define the Moho depth. In case that the velocity contrast between the lower crust and the uppermost mantle spans across a finite depth range (e.g., the Canadian Cordillera), the Moho "depth" (which is just one value, not a depth range) can differ by over 10 km depending on whether the 50%, 75%, or 100% of the velocity increase is chosen (similar examples can be found in Kao et al., 2013, JGR). I suggest the authors to quantitatively estimate the sharpness of the Moho discontinuity and its variation across Australia. The authors can also examine its spatial relationship with major tectonic/geological components and discuss the corresponding implications.
Some minor comments are given below for the authors' reference. 1. The authors use the word "wavespeed" in some places and "velocity" in other places. While technically "wavespeed" (a scalar) is the correct term, the seismological community has been using the term "velocity" to describe tomographic anomalies for decades. Whichever the authors prefer, they should be consistent throughout the text. 2. (L71, L216, and L288) The expression of "between A-B" is grammatically incorrect. It should be either "between A and B" or "in the range of A-B." 3. (L114) Citation to Figure 6d is probably erroneous. It should be Figure 6e. 4. (L275) The word "for" is missing before "its implementation details." 5. (L287) "is largest" should be "is the largest."

2022-09-14
Response to the reviews of manuscript "Next-generation seismic model of the Australian crust and implications for mineral resources and continental rifting" Reply: We thank the reviewer for your comments. From a technical point of view, this study highlights the development of an innovative workflow for ambient noise imaging, which plays an important role in improving the resolution of the seismic model. Indeed, the new seismic model forms the backbone of our study and enables us to examine the crustal structure at unprecedented resolution. We believe that this work has significant and broad implications for other areas of the globe, rather than only simply presenting a new model to the Australian geophysical community. This is the reason why we submit the paper to Nature Communications for broader readership and impact in the geoscience community.
As far as mineral deposit locations, in this paper, we summarized two possible explanations for the distribution pattern of mineral deposits in Australia. One explanation is just as the reviewer suggested that the mineral deposits are buried underneath the thick cover and only those that are exposed or shallowly buried have been discovered. This implies a great potential for mineral exploration under the cover and also highlights the necessity of resolving detailed sedimentary structures with geophysical methods.
An alternate hypothesis is that the minerals are preferentially deposited. In Australia, a recent study has reported the correlation between the craton edge and giant mines (Hoggard et al., 2021) and found a significant portion (85%) of sediment-hosted base metals occur within 200 km of the transition between thick and thin lithosphere. This view links the mineral deposit formation to deep earth process. Taking advantage of our new model, we investigate the relationship between crustal structures and mineral deposits in more detail in this revised manuscript. We extracted velocities at mineral deposit locations and compared them to those at other locations ( Figure R1). We find that the crustal velocities beneath mineral deposits are systematically faster. This is particularly evident at the upper (0-10 km) and lower (25-40 km) crustal depths. Such a relationship is reliable according to statistical tests. This observation suggests that the distribution of mineral deposits may not purely reflect the sampling bias due to shallow structures (e.g., the location where the outcrop exists) but possibly indicate a crustal-scale process. There could be a causal relationship between the deep (e.g., mantle upwelling) and shallow processes (mineral deposition). We have added this new analysis and expanded this part of the discussion in the revised manuscript (Lines 147-173).
"We further extract velocities in different depth intervals to examine if such a relationship persists to greater depths. To reduce the sampling bias caused by the clustering of deposits, the nearby deposits (within a 0.2-deg cell) are grouped to form a single sampling point. Our analysis shows that mean shear velocities are consistently faster beneath the mineral deposits than the model average ( Figure 8). The mean velocities of the mineral deposit group are 0.06 km/s faster in the shallow (0-10 km) crust and 0.03 km/s faster in the deep (30-40 km) crust and are slightly faster (about 0.01 km/s) in the middle crust (10-30 km). The reliability of the difference in mean values of the two distributions is evaluated using the t-test, which assess the validity of the hypothesis that the mean of mineral deposit group is greater than the continental average. We obtain large t scores at all depths including the middle crust where the velocity difference is the small, showing a t score of 2.94 with a p value of 0.002 (see Figure 8). The test results indicate that the observation of consistently faster crustal velocities beneath the mineral deposits is statistically significant. This distinctive pattern suggests that the mineralization process likely involves the whole crust, not just the shallow portion. A corollary is that the distribution of mineral deposits may not solely reflect the sampling bias in mineral exploration due to the presence of sedimentary cover (e.g., the location where outcrop exists). There has been growing evidence that the formation of mineral deposits is closely related to deep magmatic processes controlled by lithospheric-scale structures (Griffin et al., 2013;Hoggard et al., 2020;Groves&Santosh, 2021). For instance, a recent study has reported a close spatial association between lithospheric gradient zones and sediment-hosted deposits around the globe (Hoggard et al., 2020). In Australia, this study showed that giant mines were preferentially located within 100 km of the craton edge that marks a transition in lithospheric thickness (Hoggard et al., 2020). One possible mechanism to form such a lithospheric boundary is through a continental rifting event in an extensional setting (Mckenzie 1978;Kusznir &Ziegler, 1992), during which a basin subsides as a consequence of syn-rift mechanical stretching and post-rift isostatic re-equilibration (Kusznir &Ziegler, 1992). However, not all basins and their associated sediment-hosted deposits are related to continental rifting on the craton margin. A notable exception is the Bowen basin in eastern Australia that has undergone contemporaneous thermal and foreland-loading subsidence in the Late Permian (Brakel et al., 2009). Nonetheless, the deep-seated structures such as the basin-bounding faults could facilitate the transport of geothermal fluids and thus play a critical role for the genesis and concentration of the sediment-hosted base metal deposits near the basin margins. Away from the basin margins, there are a significant portion of mineral deposits located in high velocity areas of the cratonic region in Western Australia.
These deposits yet still form prominent clusters that align parallel to domain boundaries or elongate E-W along a high-velocity structure in central Yilgarn craton (see Figure 7a). We argue that these structural lineaments could mark weak/fracture zones that channel the mineralizing fluids and control the deposition sites." Finally, we acknowledge that much more effort is required to unlock the resources than just presented in this study. However, updated knowledge of crustal structures from a geophysical perspective marks an important step forward toward understanding the formation and deposition processes of mineral resources. We also believe that this work can also inspire future multi-disciplinary studies that involve mineralogy, geochemistry, geology, and geophysics for a more comprehensive understanding of the mineral systems in Australia.

Figure R1 The comparison of velocities beneath the mineral deposits (red) and other locations (blue) in different depth ranges. The histogram shows distribution of shear velocity and is normalized. The curves in corresponding colors are the Gaussian functions that best fit the histograms. The blue and red vertical lines indicate the mean velocity for each group. The statistic value from the t-test is indicated in the upper left corner.
As for the discussions on the upper mantle structures, when I examine the velocities of this model presented in Figure 9, the model of this study differs significantly from the other two models of AuSREM and CSEM-AU; while the latter two are very similar to each other. In addition, given the factor that there are only ~1300 paths of long-period data used in the tomography, I think the structures of the uppermost mantle in this study are not better constrained compared to the other two models. In addition, the discussions on the nature of the Tasman line seem not well supported by the model presented in Figure 9, which does not seem to clearly delineate a major velocity boundary across the Tasman line. I suggest the authors should focus their discussions more on the crustal structures.
Reply: We thank the reviewer for raising this concern about model resolution. We agree with the reviewer that the upper mantle is not the best-constrained depth range in our model. To examine the robustness of the velocity structures, we conducted a resolution test using hypothetical structures that contain a sharp velocity contrast across the Tasman line ( Figure R2). The recovered model using the same inversion parameters as the real data show that the input structures can be well recovered. This suggests that the ray-path coverage is sufficient to constrain the first-order velocity structure at longer periods.
We also agree with the reviewer that the Tasman is not characterized as a major velocity boundary in our model. In fact, the most intriguing observation is the reversal of velocity patterns across the Tasman line from shallow to deep depths. The observed large-scale velocity variation in both lateral and vertical directions is robustly constrained by our data and inversion method based on our thorough resolution analyses (see our reply to Reviewer 4 for details). Therefore, we propose a continental rifting model that can provide a possible explanation for the observed seismic structures, but this does not rule out other interpretations of the Tasman line. A well-developed discussion on the nature of the Tasman line requires more effort than presented in this work. Therefore, to make this paper more concise and focused, we follow the suggestions from Reviewers2&3 and mainly present the crustal structure and remove most of the discussions on the Tasman line.

Figure R2 (a) Input and (b) output models of the hypothesis test. The inset in (a) shows the ray-path coverage at 45 s. The inset in (b) shows the variance of seismic velocity from the trans-dimensional inversion.
We thank Reviewer 2 for the constructive comments that have helped to improve this manuscript.

Reviewer #3 (Remarks to the Author):
In this manuscript the authors developed an Australian wide shallow upper mantle Vs model by using an improved ambient noise tomography approach. Such a continental model was last developed nearly a decade ago. One significance here is the technical advance to apply cross-correlation of conventional noise correlation functions to achieve a relatively more uniform ray coverage for the continent. The resulting Vs model was then used to derive basin thickness and Moho topography across Australia, and then to infer the spatial correlation within known mineral deposits and to propose a tectonic rifting boundary across the Tasman line. The study bridges the gap across the Moho depth between early crustal and shallow upper mantle velocity models, and therefore may contribute significantly to the next generation of the Australian reference model.
Reply: We thank the reviewer for the positive comments. It is our hope that this study will provide a good example of how this advance in seismology can contribute to an improved understanding of the Earth's structure.
This reviewer raises two major points: 1 one major criticism is that several technical details seem needed to support the claims and current conclusions. Please refer to those listed in minor points below.
Reply: We provide more technical details regarding ray-path coverage, resolution analysis, interface measurement and sensitivity test in the revised manuscript. Please see our replies to the points below for further details.

Minor points include 1 Line 46 -instead of quoting their own paper (ref 22), the authors may consider the original references here.
Reply: The C 2 workflow used in this study was first proposed in our previous work (Chen and Saygin, 2020). We have cited the paper by Zhang et al. (2020) that proposed a similar idea as our study.
2 Section "Improvement of ray path coverage" -curious why the C2 coverage drops significantly after 24 s (or so). Presumably the permanent network stations are used as virtual sources so the signal to noise ratio should be good enough.
Reply: We thank the reviewer for raising this important question. This is mainly caused by the procedure of C 2 calculation, which correlates (again) the correlation functions from different frequency bands. In particular, the short-period stations that are deployed mainly in eastern Australia count for a significant portion of the data used in our study ( Figure R4). These stations typically have a corner frequency of around 10 s, hence the longer-period information cannot be preserved. Excluding the short-period stations may help improve the SNR at longer periods. This, however, also reduces the number of stations used in C 2 , which in turn limits the SNR. Improvement to SNR can be potentially made by designing a workflow that can distinguish the long-and short-period stations and apply a weighted stacking scheme (e.g., based on frequency content). This could help preserve a broad frequency bandwidth while ensuring sufficient stacks during the C 2 calculation. We have clarified this point in Lines 317-321 of the revised manuscript.
"The mixture of signals recorded by broadband and short-period instruments in the higher-order crosscorrelation calculation limits the bandwidth of the C 2 functions and hence long-period dispersion measurements (above 30 s) from C 2 are not used in the inversion. This issue can be potentially alleviated by optimizing the workflow by, for example, selecting only broadband stations as virtual sources and applying weighted stacking according to the frequency content of individual C 2 functions. These approaches will be investigated in a future study." 3 In the same section, about the longer period data -the authors claim in the last generation noise model, the data were only down to 30 s. In Figure 1b a lot more stations are used, but how many are broadband? Please provide ray coverage maps for longer periods.
Reply: Figure R4 shows the broadband and short-period stations, which are obtained from the AusPass website. Most of the transportable arrays in Australia are short-period stations, whereas the recently deployed permanent stations (e.g., S1 and AU networks) are primarily broadband stations. We have included the ray-path coverage in the group velocity results (Figure 3 in the revised manuscript).  Reply: We thank the reviewer for raising this concern. We have designed a hypothesis test to examine if the unbalanced station coverage can affect the resolvability of the uppermost mantle structures. We introduce bands of low and high-velocity structures with widths of 4 degrees to either side of the Tasman line (TL) (Figure R2a). We consider the station distribution at the long period of 45 s, at which the ray path coverage is high across the TL. The inversion can robustly recover the structural variation well and shows the minimum uncertainty across the TL ( Figure R2b). This test suggests that the large-scale structures are well constrained at longer periods and the observations near the TL are valid.
In this study, we only provide a possible interpretation for the nature of TL that can is consistent with our seismic observations reasonably well. A more detailed interpretation of TL requires evaluating other mechanisms in the context of new seismic observations, which is beyond the scope of the current study.
To make this paper more concise and the theme more focused, we follow the suggestions from Reviewers 2&3 and mainly present the crustal portion of the model. We now only report the robust observations in the uppermost mantle and remove the discussions of the TL. Reply: We thank the reviewer for pointing this out. The difference in absolute velocity between our model and AuSREM can be attributed to two main factors: 1) The shear velocity of AuSREM is mainly constructed from receiver function (RF) inversion and ambient noise tomography. While the RF inversion is sensitive to the crustal interface, it is less reliable in terms of absolute velocity. 2) The previous ambient noise model was constructed with about 10% of the ray paths used in this study. The long-period information is particularly sparse in the previous model. Hence, the dispersion data is mainly used to constrain the upper 20 km in AuSREM, and the shear velocities from the ambient noise model below 30 km are not incorporated in AuSREM.
In this revision, we found that the initial model overestimated the upper mantle velocities due to the linear extrapolation of lower crust velocity. We have reconstructed the initial model and run the inversion. The updated model leads to slightly different velocity structures at lower crust/uppermost mantle depths. The results of the velocity comparison are shown in Figure R6 (also see Figure 6 in the revised manuscript).

Figure R6 Comparison of upper, middle and lower crustal structures between (a)-(c) our model and (d)-(f) AuSRREM (Salmon et al., 2013). (g)-(i) The corresponding difference between the two models. The black dashed lines in (a) and (d) indicates velocity contours of 3.45 km/s
We apologize for the confusing definition of the velocity contour. We did not use a constant velocity contour to determine the Moho depth in our study. Instead, we used varying velocity values to consider the lateral variation in average velocity across the continent. In this revision, we follow the suggestion from Reviewer 4 and use a more objective criterion that invokes velocity jump to determine the Moho. The Moho is determined by the depth of 50% velocity jump from the lower crust to upper mantle velocities. This leads to a different Moho depth map compared to that obtained previously ( Figure R12; also see Figure 9 in the revised manuscript).

Point 6 above brings a question about the resolving power of the current dataset. In addition to plotting depth sensitivity kernels, the authors may consider inverting simple 1D synthetic velocity profiles to illustrate 1) the depth of sedimentary basins; and 2) the 40-70 km velocity contrasts across the Tasman line can be satisfactorily recovered.
Reply: We thank the reviewer for these suggestions. We have designed two synthetic tests to examine the resolving power of our dataset and provided these tests in the supplementary material. In the first test, we vary the sediment thicknesses from 2 to 10 km ( Figure R7), which represents a typical depth range observed in our study. We then generate a synthetic dispersion curve for each model and invert for shear velocities using the same parameters as those used in the real data cases. The test results show that the low-velocity sedimentary basin structures can be well recovered, indicating that the shallow structures are well constrained by our short-period data.

Figure R7 Inversion of 1D velocity structure with varying sediment thickness.
In the second test, we systematically vary the velocity from -10% to 10% in the upper mantle depth (40-70 km) ( Figure R8). The inversion successfully recovered the trend of velocity but failed to resolve the discontinuities. This is expected from the inversion of the surface wave dispersion curve which is sensitive to average velocity over a certain depth range (depending on the frequency) rather than sharp boundaries. This test suggests our data is sufficient to distinguish the first-order shear velocity contrast in the uppermost mantle.

Figure R8 Inversion of 1D velocity structure with varying velocity perturbations in upper mantle.
8 Not clear the point Figure 7b and corresponding text try to make? At the first glance there does not seem to be deposits coincide with slow velocities (red). Reply: We used Figure 7b to quantitatively assess the relationship between crustal velocity and mineral deposit location. There are only a small portion (26%) of the mineral deposits that fall within or, more precisely speaking, hit the margin of the low-velocity zone. As reviewer suggested, the vast majority of mineral deposits are underlain by relatively high crustal velocities.
We have added a new figure ( Figure R1; also see Figure 8 in the revised manuscript) to better demonstrate this relationship and support our point. We find that the crustal velocities are systematically faster at the mineral deposit locations. This observation suggests that the mineral deposition process may involve the whole crust not only the shallow structures. Please see our reply to the question from Reviewer 2 for more details.

Line 153 the choice of contour values (3.1 -3.3. km/s) for the basement depth -why for the Moho is the Vs contour fixed?
Reply: We apologize for the confusion. The Moho depth is in fact determined using a range of velocities not a fixed value in order to consider the lateral variation in average crustal velocity.

in the last section about the mantle structure -this is really not the prime region the current inversion can cover.
Reply: We agree with the reviewer. This concern was also raised by Reviewer 2. To address this point, we have 1) conducted resolution analysis and only reported robust observations in this depth range, 2) removed the discussions on the nature of the Tasman line and its implication on continental rifting of the Australian continent, and 3) conducted more quantitative analysis of our model and expanded the discussions on the crustal structures per the suggestions from Reviewer 4.

Reviewer #4 (Remarks to the Author):
Comments on "Next-generation seismic model of the Australian crust and implications for mineral resources and continental rifting" by Chen et al.

This paper presents a 3-D shear-wave velocity model of the Australian continent based on the latest results of ambient seismic noise tomography inversion. The authors adopted an innovative approach to increase data coverage by reconstructing the noise correlation functions between a pair of temporal stations deployed at different times but overlapped with a few permanent stations. This approach significantly improves the raypath density that, in turn, leads to higher resolution of the tomography images. With the tomography results, the authors explain the highlights of the new model and discuss their implications for regional tectonics and mineral resources. Overall, this study is a significant step forward in our understanding of the detailed crustal and uppermost mantle velocity structures of the region. Before it is accepted for publication, however, I would like to point out a few issues that should be addressed and/or elaborated in more detail.
Reply: We thank the reviewer for acknowledging the contribution of our study. We have addressed the concerns raised. Please see below for details. Fig. 6 is that they roughly correspond to 0-10, 10-25, and 25-40 km, respectively. However, I think that the authors should be able to quantitatively define the 3-D geometry and thickness variation of the upper, middle, and lower crust for most part of the studied areas. The geometry and thickness variation can also provide important constraints on the characteristics of major geological structures (e.g., cratons, basins, and orogenic belts) and/or regional tectonic evolution.

First of all, it is unclear how the authors define the ranges of upper, middle, and lower crust based on their results. My impression from
Reply: We thank the reviewer for this comment. The reviewer is correct that we divided the model into different layers according to their presumed depth ranges. This is mainly for the purpose of ease of comparison for different models. We have followed the reviewer's suggestion and conducted quantitative analysis of our model to determine the 3D geometry of crustal interfaces. For 1D velocity profile at each inversion grid point, we locate the depths of maximum velocity gradient in upper and lower crust ( Figure  R9). Because the middle crust is generally characterized by less heterogeneous structures, we define the center of the middle crust as the depth with the lowest velocity gradient (local minimum). Then the crustal boundaries are determined by the midpoint of the local maximum and minimum. This criterion leads to robust measurements at most of the grid points. The resulting crustal interfaces are shown in Figure R10.

The depth variation of crustal interfaces is generally similar to that of the Moho. The crustal interfaces are elevated in the cratonic region of western and northern Australia and are depressed in central
Australia where the Moho is also the deepest. We have added these analyses and discussions on Lines 193-198 the revised manuscript.
"We determine, for the first time, major crustal layering of the Australian continent using a velocity gradient approach (see Method section). The shallow crustal interface that approximately divides the upper and middle crust is on average 10 km deep, and is generally deeper beneath the sedimentary basins and shallower in the cratonic regions in western, northern and southern Australia (Figure 9a). The lower interface separating the middle and lower crust resides at about 27 km depth, which generally mimics the shallower layer with the most significant depression observed in central and southeastern Australia (Figure 9b)."

Figure R10 Crustal layering determined from this study and previous work. Crustal interfaces of (a) upper-middle crust and (b) middle-lower crust.
Secondly, it is confusing when the authors discuss the spatial relationship between velocity anomalies and mineral deposits. Specifically, the authors point out in L145 that giant mineral deposits are preferentially located within 100 km of the craton edge (presumably along the edge of high-velocity anomalies), but later state in L149-150 that the deposits are found near basin margins (i.e., the edge of low-velocity anomalies). The text between L146 and L149 explains that basins can form along craton edge zones due to continental rifting. However, Fig. 1a clearly show that not all basins are located along craton edge zones. Fig. 7 also show that many mineral deposits (especially those in western Australia) are within the high-velocity craton. Thus, I am not sure if a generalized relationship can be derived.
Reply: We thank the reviewer for this comment. We apologize for the confusion. The arguments on L145 (high velocity) and L149-150 refer to observations at different depths. The argument on L145 was quoted from a recent study (Hoggard et al., 2020), which reported that the giant mineral deposits were preferentially located near the craton edges (see figure below). The location of high-velocity craton edge is mapped at upper mantle depths from shear velocity SL2013sv (Schaeffer and Lebedev, 2013), not our model. The argument on L149-150 refers to low-velocity sedimentary basin from our model.

The text from L146-149 indeed associates the basin formation with continental rifting. The rift basin formed close to the craton margin can explain some giant mineral deposits like those reported in Hoggard et al. (2020)
. However, with respect to all deposits in Australia, these arguments are inaccurate considering variable mechanisms of basin subsidence across the continent. We intend to say that there is likely a structural control on the mineral deposition process. There has been increasing agreement that the spatial distribution of mineral deposits is closely related to lithospheric-scale structure. The basin margin, where large bounding faults could exist, provides a potential pathway for the transfer of mineralizing fluids. We emphasize that the presence of large-scale faults is not necessarily related to basin margin. As pointed out by the reviewer, the mineral deposits in western Australia are not associated with any low-velocity structures. Rather they are located either along the domain boundaries or along linear high-velocity structures found in this study. These structural lineaments could provide weak/fracture zones that channel the mineralizing fluids. As suggested by the reviewer, we cannot derive a general relationship between different sizes/types of mineral deposits and structural types in this study. Our purpose is to examine if there is a systematic trend in seismic velocity distribution at mineral deposit locations, which could help us improve the understanding of the potential first-order structural control on mineralization. We have clarified this point and rewritten the discussions on Lines 144-173 of the revised manuscript.
Finally, the comparison of Moho depth with previous models is very useful. But there are cases where the extreme values should be treated with caution. For example, the new model has many places with the Moho depths larger than 55-60 km (Fig. 8). How confident can we trust these values? It is worth noting that some of these extreme values may be related to the way we define the Moho depth. In case that the velocity contrast between the lower crust and the uppermost mantle spans across a finite depth range (e.g., the Canadian Cordillera), the Moho "depth" (which is just one value, not a depth range) can differ by over 10 km depending on whether the 50%, 75%, or 100% of the velocity increase is chosen (similar examples can be found in Kao et al., 2013, JGR). I suggest the authors to quantitatively estimate the sharpness of the Moho discontinuity and its variation across Australia. The authors can also examine its spatial relationship with major tectonic/geological components and discuss the corresponding implications.
Reply: We thank the reviewer for these comments. The extreme values in Moho depths are measurement outliers caused by the instability of our previous depth determination approach. We have followed the approach proposed by Kao et al. (2013) and determined the depth of Moho using the velocity jump. Figure  R11 shows an example of depth measurement. Similar to Kao et al. (2013), we used a 50% or 85% increase from lower crust to upper mantle velocities as a proxy of Moho depths. We found that an 85% velocity increase overestimates the Moho depth as compared to existing constraints, and a 50% velocity increase provides a good estimate of the Moho ( Figure R12). "The sensitivity of dispersion data to deep structure (supplementary Figures S10 and S12) enables us to characterize the transition in physical properties from crust to mantle. We measure the transition thickness (i.e., Moho sharpness) and its corresponding velocity variation by considering the depth and velocity difference between the 50% and 85% of velocity increases from crust to mantle (Kao et al., 2013). The Moho sharpness (Figure 10a) is generally anti-correlated with the velocity jump (Figure 10b), wherein a smaller velocity increase leads to a sharper boundary and vice versa. The Moho sharpness map shows large (> 6 km) transition thicknesses in western, northern and eastern Australia, whereas zones of relatively thin (< 4 km) crust-mantle transition dominate central Australia and extend southward to the coastline (see Figure 10a). Our measurements from ambient noise imaging are compared with the constraints from receiver functions that classify the crust-mantle transition into four distinctive groups (Kennett & Saygin, 2015). Receiver function imaging reveals large variation in transition thickness across the continent and at a regional scale of hundreds of kilometers. Similar observations between the two studies include 1) sharp (2-4 km) Moho along the northern and southeastern edges of the Yilgarn craton and considerable variability in the cratonic interior of Western Australia, 2) sharp Moho in southern Australia, particularly in the vicinity of the Gawler craton, and 3) thick transition regions beneath the sedimentary basins (e.g., Eramanga and Cooper basins) in eastern Australia. Overall, our observations do not show a clear relationship of Moho sharpness to tectonic age. For example, a thick crust-mantle transition is observed beneath both Archean and Phanerozoic basements. This could suggest that the rheological properties near the base of crust are not only inherited from crustal formation but may have undergone substantial reworking during the secular evolution of the continental crust (Kennett & Saygin, 2015)." Overall, benefiting from the reviewer's suggestions, we are able to construct a 3D model of crustal geometry ( Figure R14). This model summarizes the key contributions of our study and provides a more comprehensive view of the crustal structures of the Australian continent.  Some minor comments are given below for the authors' reference. 1. The authors use the word "wavespeed" in some places and "velocity" in other places. While technically "wavespeed" (a scalar) is the correct term, the seismological community has been using the term "velocity" to describe tomographic anomalies for decades. Whichever the authors prefer, they should be consistent throughout the text. Reply: We thank the reviewer for this comment. We have used "velocity" consistently in the revised manuscript.

Indeed
More importantly, where are these "abnormal" Vs grid points located geographically? Are they simply randomly scattered across the entire continent (I guest not!), or do they appear to cluster in specific regions? Another important question is the robustness of these abnormal Vs models. Are they mainly artifacts due to imperfect inversion or poor data resolution? A direct comparison of the Vs models derived by this study and that by previous studies with receiver function inversion for some of the representative grid points would probably help us assess the quality and robustness of individual Vs models.
Reply: We thank the reviewer for the comment on the velocity model. We have carefully examined our measurements of both crustal interfaces and the Moho. For the intra-crustal discontinuities, we have slightly modified the selection criteria by reducing the threshold of minimum velocity gradient that are used to identify the peaks (local maxima). This helps to improve the interface picking in south Australia near the Gawler craton where the velocity gradient perturbation is relatively small. As a result, we obtained reliable measurements at over 90% of the inversion nodes (a total of 2520), with 2268 and 2416 good measurements for the shallow and deep crustal interfaces, respectively ( Figure R1). For the nodes that are not well constrained, the underlying crustal structures are typically characterized by velocity with small perturbations, hence smoothly varying velocity gradient without distinctive extrema. These nodes are generally located in cratonic regions of western Australia as well offshore regions that are not well covered by ray paths ( Figure R2). We have clarified this point in the revised manuscript in line 360-363. The plot of cross-section of gradient measurements is provided in the supplementary material.
"The two interfaces well delineate the transition region from high to low velocity gradients (supplementary Figure S14). Our approach leads to reliable measurements at over 90% of grid points (see Figures 9a and b). Nodes that are not well constrained are typically characterized by velocity profiles with small perturbations, hence smoothly varying velocity gradient without distinctive extrema."  "The cases of representative velocity profiles with thick, thin and undefined Moho transition are demonstrated in supplementary Figure S15. Comparisons of our velocity profile with those obtained from receiver function inversions at nearby stations show that sharp velocity jump typically falls within the depth range determined from our model, and is closer to the shallow boundary (i.e., Z50; supplementary Figures S16-S21). Hence, we adopt the shallow one as a proxy of the Moho depth. The difference between the two interfaces provides an estimate of the sharpness of the crust-mantle transition. We obtain reliable Moho depth measurements at about 90% of the inversion nodes. The undefined nodes are mainly caused by a lack of clear velocity gradient in the lower crust (see supplementary Figures S15e-f), which are located near the continental margins where the data coverage is poor (see Figure 10). The northern part of the Yilgarn craton in western Australia also exhibits a smooth crust-mantle transition, which prohibits the determination of reliable Moho transition thickness."  The transition thickness is obtained by taking the depth difference between the 50% and 85% increase from crust to mantle velocities. The gray areas mask the region where the Moho depth measurements are not reliable. The transition velocity is defined similarly by calculating the velocity difference. The green diamonds in (a) are the transition thickness determined from receiver functions from Kennett and Saygin (2015). The blue stars mark the locations of representative velocity profiles demonstrating the characteristics of Moho transition ( Figure R3). The red stars mark the stations where shear velocities from receiver function inversions are compared with our velocity model ( Figures R5-R8).
Finally, we have compared the velocity profiles with earlier constraints from receiver function inversions (RFs). We digitized the shear velocity profiles reported from literature at a few targeting locations across the continent. First of all, the velocity profiles obtained from ambient noise tomography (ANT) are much smoother compared to those from receiver function inversions. Specifically, ANT is insensitive to sharp dV 85%-50% discontinuities that can be often recovered from RFs. At the same time, we notice that the RF inversion results in Australia can exhibit large variabilities among nearby stations. For example, crustal velocities at stations WT09 and WT10 deployed in eastern Yilgarn craton (Western Australia) are respectively higher and lower than that of our model ( Figure R5). Our velocity profile typically falls within the range of velocities determined from nearby stations (e.g., stations WT03, WT04, WT05 in western Yilgarn craton). This suggests that the velocity model from ANT represents a regional average structure instead of a point-based measurement as from RF inversion. The difference between ANT and RF inversion results can also be a consequence of different sensitivity of the two methods. Surface wave dispersion curve is more sensitive to absolute velocities of the crust whereas RF is mainly sensitive to sharp discontinuities (i.e., velocity gradient). This is particularly evident from the station BL10 in central Australia ( Figure R8), where we observe a large difference between the two velocity profiles. We find that our velocity profiles are generally in good agreement with those from AuSREM that constrain absolute crustal velocities by integrating a variety of observations. The Moho depth from RFs typically falls within the depth range determined by the 50% and 85% velocity jumps, and is closer to the shallow boundary (i.e., Z50; Figures R5 and R7). However, we also found large differences at stations deployed in the Pilbara craton ( Figure R6), where depth measured from ANT is considerably deeper (by 5-10 km). This could suggest that the long-period dispersion information is not well retrieved in this region. This also highlights the necessity of jointly inverting multiple observations (e.g., receiver functions, surface wave dispersion curves, auto-correlation functions) to reconcile the resolution and sensitivity of different methods and develop a more accurate model in the future. We have added these plots to supplementary material.